Sharad Chitlangia

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About

Sharad Chitlangia is a researcher at the forefront of computational advertising and machine learning systems, with a focus on combating fraudulent traffic in digital ecosystems. His key research areas include real-time anomaly detection, weakly supervised learning, and scalable AI architectures for online advertising. Chitlangia’s most notable contribution is the development of **SLIDR (SLIce-Level Detection of Robots)**, a real-time deep neural network model designed to detect robotic traffic at scale. This work, published in 2023, addresses the critical challenge of identifying fraudulent ad interactions with high precision and speed, using weak supervision to adapt to evolving traffic patterns. The paper has garnered early citations, reflecting its practical relevance in the advertising industry. Chitlangia’s approach stands out for its scalability and comprehensiveness, enabling rapid response to changing fraud tactics. His research bridges the gap between theoretical machine learning and real-world deployment, making a tangible impact on the integrity of online advertising. With a growing citation footprint, Chitlangia is establishing himself as a key voice in the fight against ad fraud, and his work continues to influence both academic research and industry practices in computational advertising.